A novel approach for semi-quantitative assessment of reliability of blood flow values in DCE-CT perfusion

The Primacy of High B-Value 3T-DWI Radiomics in the Prediction of Clinically Significant Prostate Cancer

Predicting clinically significant prostate cancer (csPCa) is crucial in PCa management. 3T-magnetic resonance (MR) systems may have a novel role in quantitative imaging and early csPCa prediction, accordingly. In this study, we develop a radiomic model for predicting csPCa based solely on native b2000 diffusion weighted imaging (DWI_b2000) and debate the effectiveness of apparent diffusion coefficient (ADC) in the same task. In total, 105 patients were retrospectively enrolled between January–November 2020, with confirmed csPCa or ncsPCa based on biopsy. DWI_b2000 and ADC images acquired with a 3T-MRI were analyzed by computing 84 local first-order radiomic features (RFs). Two predictive models were built based on DWI_b2000 and ADC, separately. Relevant RFs were selected through LASSO, a support vector machine (SVM) classifier was trained using repeated 3-fold cross validation (CV) and validated on a holdout set. The SVM models rely on a single couple of uncorrelated RFs (ρ < 0.15) selected through Wilcoxon rank-sum test (p ≤ 0.05) with Holm–Bonferroni correction. On the holdout set, while the ADC model yielded AUC = 0.76 (95% CI, 0.63–0.96), the DWI_b2000 model reached AUC = 0.84 (95% CI, 0.63–0.90), with specificity = 75%, sensitivity = 90%, and informedness = 0.65. This study establishes the primary role of 3T-DWI_b2000 in PCa quantitative analyses, whilst ADC can remain the leading sequence for detection.

CT Perfusion in Patients with Lung Cancer: Squamous Cell Carcinoma and Adenocarcinoma Show a Different Blood Flow

Objectives

To characterize tumour baseline blood flow (BF) in two lung cancer subtypes, adenocarcinoma (AC) and squamous cell carcinoma (SCC), also investigating those “borderline” cases whose perfusion value is closer to the group mean of the other histotype.

Materials and Methods

26 patients (age range 36-81 years) with primary Non-Small Cell Lung Cancer (NSCLC), subdivided into 19 AC and 7 SCC, were enrolled in this study and underwent a CT perfusion, at diagnosis. BF values were computed according to the maximum-slope method and unreliable values (e.g., arising from artefacts or vessels) were automatically removed. The one-tail Welch's t-test (p-value <0.05) was employed for statistical assessment.

Results

At diagnosis, mean BF values (in [mL/min/100g]) of AC group [(83.5 ± 29.4)] are significantly greater than those of SCC subtype [(57.0 ± 27.2)] (p-value = 0.02). However, two central SCCs undergoing artefacts from vena cava and pulmonary artery have an artificially increased mean BF.

Conclusions

The different hemodynamic behaviour of AC and SCC should be considered as a biomarker supporting treatment planning to select the patients, mainly with AC, that would most benefit from antiangiogenic therapies. The significance of results was achieved by automatically detecting and excluding artefactual BF values.

Multislice Analysis of Blood Flow Values in CT Perfusion Studies of Lung Cancer

Objectives. Tumour heterogeneity represents a key issue in CT perfusion (CTp), where all studies are usually based on global mean or median values of perfusion maps, often computed on whole tumour. We sought to determine whether, and to what extent, such global values can be representative of tumour heterogeneity, with respect to single slices, and could be used for therapy assessment. Materials and Methods. Twelve patients with one primary non-small cell lung cancer lesion were enrolled in this study, for a total amount of 26 CTp examinations and 118 slices. Mean and median blood flow (BF) values, calculated voxel-based, were computed on each slice and the whole tumour. To measure functional heterogeneity, entropy was calculated on BF values as well. Results. Most of the slices were not represented by the global BF values computed on the whole tumour. In addition, there are a number of lesions having equivalent global BF values, but they are composed of slices having very different heterogeneity distributions, that is, entropy values. Conclusions. Global mean/median BF values of the single slices separately should be considered for clinical assessment, only if interpreted through entropy computed on BF values. The numerical equivalence between global BF values of different lesions may correspond to different clinical status, thus inducing possible errors in choice of therapy when considering global values only.